We describe a constraint-based tagging approach where individual constraint rules vote on sequences of matching tokens and tags.
Disambiguation of all tokens in a sentence is performed at the very end by selecting tags that appear on the path that receives the high- est vote.
This constraint application paradigm makes the outcome of the disambiguation in- dependent of the rule sequence, and hence re- lieves the rule developer from worrying about potentially conflicting rule sequencing.
The ap- proach can also combine statistically and manu- ally obtained constraints, and incorporate neg- ative constraint rules to rule out certain pat- terns.
We have applied this approach to tagging English text from the Wall Street Journal and the Brown Corpora.
Our results from the Wall Street Journal Co...